Visualization.Rmd

---
title: "Exploratory Data Analysis"
output: 
  html_document:
    theme: journal
    css: styles.css
    code_folding: hide
---

<!-- Tasks:  -->
<!-- (answer the questions found in the edX part using Python chunks) -->
<!-- (this is the theory of EDA, follow some steps https://r4ds.had.co.nz/exploratory-data-analysis.html) -->
<!-- (create a dashboard using Shiny?) -->

```{r, echo=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
```


## Introduction 

EDA is an important part of any data analysis. It consists an iterative cycle, where we: (a) generate questions about your data, (b) search for answers by visualising, transforming, and modelling your data; and (c), Use what you learn to refine your questions and/or generate new questions. Here we'll explore the data to see if we can find any good insights to share with the marketing department. 


## About the Data 

The dataset found online consists of three files, containing data that was collected on January 1st 1998.

* AdvWorksCusts.csv : Customer demographic data.
* AW_AveMonthSpend.csv: sales data containing the amount of money the customer spends with Adventure Works Cycles on average each month.
* AW_BikeBuyer.csv: contains sales data in the form of a Boolean flag indicating whether a customer has previously purchased a bike (1) or not (0).


## Data Wrangling

<!-- Clean the data by replacing any missing values and removing duplicate rows. In this dataset, each customer is identified by a unique customer ID. The most recent version of a duplicated record should be retained. -->

```{r}
library(readr)
library(dbplyr)
library(ggplot2)
library(tidyverse)
library(ggplot2)
library(readr)
library(data.table)
library(DT)
library(pander)
library(scales)
library(cowplot)
library(shiny)
```


```{r}
customer <- read_csv("~/CS 499 Senior Project/datasets/AdvWorksCusts.csv")
spend <- read_csv("~/CS 499 Senior Project/datasets/AW_AveMonthSpend.csv")
bikebuyer <- read_csv("~/CS 499 Senior Project/datasets/AW_BikeBuyer.csv")
three_datasets <- data.frame(customer, spend, bikebuyer)
data_clean <- select(three_datasets,-c(CustomerID.1, CustomerID.2))
missing_values <- sapply(data_clean, function(x) sum(is.na(x))) #it checks number of missing values by column
data_clean <- select(three_datasets,-c(CustomerID.1, CustomerID.2, Title, MiddleName, Suffix, AddressLine2)) 
data_clean <- data_clean[!duplicated(data_clean), ] #it removes duplicates
```

## Data Insights 

```{r}

#min(data_clean$AveMonthSpend)
#max(data_clean$AveMonthSpend)
#mean(data_clean$AveMonthSpend)
#median(data_clean$AveMonthSpend)

pander(summary(data_clean$AveMonthSpend))

#sd(data_clean$AveMonthSpend)


```

The distribution of the values in the BikeBuyer column:
```{r}
ggplot(data_clean, aes(x = as.factor(BikeBuyer))) +
  geom_histogram(binwidth = .5, colour = "blue", fill = "white", stat = 'count')
```
Insight: fewer customers have bought bikes than have not bought bikes. We should consider adveritising our bikes more, rather than just the parts. 


mean YearlyIncome by Occupation:
```{r}

ggplot(data_clean, aes(x = forcats::fct_reorder(Occupation, YearlyIncome, .fun=median), y = YearlyIncome))  + 
  geom_boxplot( width = .5, fill="tomato3")  + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6))
```
Insight: from lowest to highest: Manual, Clerical, Skilled Manual, Professional, Management. 


Average month spend by gender 

```{r}
g <- ggplot(data_clean, aes(AveMonthSpend))
g + geom_density(aes(fill=factor(Gender)), alpha=0.8) + 
    labs(title = "Density plot", 
         subtitle = "",
         caption = "Source: mpg",
         x = "Average Month Spend",
         fill = "Gender")
```
INsight: Males on average tend to spend more (age between 25-45, find a way to divide it between >25, 25-45, 46<).


```{r}
ggplot(data_clean, aes(x = MaritalStatus, y = AveMonthSpend))  + 
  geom_bar(stat = "identity", width = .5, fill="tomato3")  + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  facet_wrap(~Gender) 
```
INsight: Married Male spend more
People with no children at home spend more.